Embodiments of the present disclosure relate to imaging, and more particularly to the identification of an optimal image frame for ultrasound imaging.
As will be appreciated, ultrasound imaging has been employed for a wide variety of applications. During the process of ultrasound scanning, a clinician attempts to capture a view of a certain anatomy which confirms/negates a particular medical condition. Once the clinician is satisfied with the quality of the view or the scan plane, the image is frozen to proceed to the measurement phase. For example, ultrasound images are routinely used to assess gestational age (GA) and weight of a fetus or to monitor cardiac health of a patient. Ultrasound measurements of specific features of fetal anatomy such as the head, abdomen or the femur from two-dimensional (2D) or three-dimensional (3D) image data are used in the determination of GA, assessment of growth patterns and identification of anomalies. Similarly, for cardiac applications, thicknesses of cardiac walls are routinely measured by cardiologists to check for cardiomyopathy.
Image acquisition is quite a challenging problem for sonographers. Currently, image acquisition takes anywhere between 1 to 5 minutes for each correct scan plane acquisition and more so for novice clinicians. The other challenge the less experienced clinicians/sonographers face is the ability to correctly identify acceptable scan plane frames. It is also desirable for the clinicians to have an understanding of how far they are from correct scan plane. Moreover, ultrasound images are subject to both patient and operator/clinician variability. Also, determining a quality of an image frame is fraught with challenges. Particularly, pixel intensities in the images vary significantly with different gain settings.
Currently, there exist semi-automated and automated techniques for ultrasound image analysis. However, ultrasound images, such as fetal ultrasound images are invariably contaminated by a number of factors that can compromise a diagnosis. The contaminants may include factors, such as, but are not limited to, near field haze due to fat deposits, unpredictable patient movement, and the ubiquitous speckle noise. Operator variability also limits reproducibility of ultrasound imagery and measurement. There are multiple reasons for the inter-operator variability. Firstly, two-dimensional (2D) echocardiography visualizes only a cross-sectional slice of a three-dimensional structure, commonly referred to as the scan plane. Even small changes in positioning of the transducer, which has six degrees of freedom, may lead to significant changes in the scene visualized, which may in turn lead to incorrect measurement. In addition, sub-optimal ultrasound image settings such as gain, time-gain compensation may decrease the ability to visualize the internal structures of the human body.
Early efforts at improving robustness and accuracy of clinical workflow have tended to focus on semi-automated methods that include, for example, femur segmentation, head segmentation and cardiac segmentation. However, the above processes tend to be time-consuming. Additionally, use of these techniques may entail user intervention or call for a trained sonographer. These techniques may also be subject to operator variability or may be prone to false detection. In remote or rural markets it may be particularly difficult to obtain services of a trained ultrasonographer or ultrasound technician, causing remote regions to be poorly served or underserved.